---
title: Import model packages into MLOps
description: Export a model created with DataRobot AutoML for import as a model package (.mlpkg file) in standalone MLOps environments.
section_name: MLOps
maturity: public-preview
platform: self-managed-only

---

# Import model packages into MLOps {: #import-model-packages-into-mlops }

!!! info "Availability information"
	This feature is _only available for Self-Managed AI Platform users_ that require MLOps and AutoML to run in separate environments. The process outlined requires multiple feature preview flags. Contact your DataRobot representative for more information about this configuration.

	**Feature flags**: Contact your DataRobot representative.

Models created with DataRobot AutoML can be [exported](#export-a-model) as a model package (.mlpkg file). This allows you to [import](#import-a-model-package-to-a-dataRobot-mlops-only-environment) a model package into standalone environments like DataRobot MLOps to make predictions and monitor the model. You can also [create a new deployment in MLOps](#import-a-model-package-to-a-dataRobot-mlops-only-environment) by importing a model package.

##  Export a model from AutoML {: #export-a-model-from-automl }

You can export models created with AutoML from the **Deploy** tab on the model's **Predict** page.

!!! note
	The **MLOps Package** option on the **Predict** > **Downloads** tab directs you to **Open the Deploy tab** where you can deploy the model, add it to the **Model Registry**, or download the model package.

To export your model a model package (.mlpkg) file from DataRobot AutoML, add it to the **Model Registry**, or deploy it directly to the **Deployments** inventory, take the following steps:

1. On the **Leaderboard**, click the model you want to export.

2. Click **Predict** > **Deploy**.

3. On the **Deploy** tab, there are three options available::

	![](images/model-pkg-6.png)

	| | Element | Description |
	|-|---------|-------------|
	|![](images/icon-1.png) | Deploy model | Deploy your model to the [**Deployments** inventory](deploy-inventory) in DataRobot AutoML. |
	|![](images/icon-2.png) | Add to Model Registry | Register your model as a model package in the [**Model Registry**](registry/index) to deploy later or to use the model package to replace a model for an existing deployment (if the model package is eligible). |
	|![](images/icon-3.png) | Download .mlpkg | Generate and download your model package for deployment creation with DataRobot MLOps. |

4. Click **Download .mlpkg**, to prepare the model package for export. View your progress in the Worker Queue under **Processing**.

	![](images/model-pkg-7.png)

After DataRobot finishes generating the model package, the download begins automatically, appearing in the downloads bar when complete. You now have an exported model package, fully capable of deployment to a different environment (such as DataRobot MLOps).

##  Import a model package to a DataRobot MLOps-only environment {: #import-a-model-package-to-a-datarobot-mlops-only-environment }

To add an exported .mlpkg file to DataRobot MLOps as a [model package](reg-create):

1. Click **Model Registry** and then click **Model Packages**.

2. On the **Model Packages** tab, click **Add new package** and then click **Import model package file (.mlpkg)**.

3. Browse for and upload, or drag-and-drop, the .mlpkg file you exported from DataRobot AutoML.

	![](images/reg-transfer-2.png)

	The model package is uploaded and extracted.

4. When this process completes, DataRobot adds your model package to the **Model Packages** tab, complete with the metadata for your model package.

	![](images/reg-transfer-3.png)

##  Deploy a model package in MLOps {: #deploy-a-model-package-in-mlops }

To import your model into DataRobot MLOps, you can add it as a new [deployment](deploy-methods/index).

1. Navigate to the **Deployments** page and click **Add deployment**.

	![](images/model-pkg-4.png)

2. Under the **Add a model** header, click **Browse** and click **Local file** to upload your model package. You can also drag and drop a model package file into the **Add a model** box.

	![](images/model-pkg-3.png)

3. After you upload a model, the **Deployments** tab opens.

	!!! note
		The information under the **Model** header appears automatically, as your model package contains that metadata. The model package also supplies the training data; you don't need to provide that information on this page. You can, however, add outcome data after you deploy the model.

4. Configure the [deployment creation settings](add-deploy-info) and decide if you want to allow [data drift](data-drift) tracking or require an [association ID](accuracy-settings.md#association-id) in prediction requests.

5. When you have added information about your data and your model is fully defined, you can click **Deploy model** at the top of the screen.